Keywords: Analysis/Processing, Machine Learning/Artificial Intelligence
Motivation: A previously developed MRI method for quantitative myocardial oxygen extraction mapping (mOEF) showed promising results, but image quality suffered from distortion and inhomogeneity artifacts.
Goal(s): The objective of this study is to evaluate a new CMR method for in vivo measurement of mOEF utilizing on a deep-learning quantification approach in healthy controls.
Approach: A new pulse sequence and a novel deep learning-based analysis method were created and evaluated on a group of healthy subjects.
Results: This investigation yielded dramatically improved image quality, which allowed reliable evaluation of reproducibility and distribution of mOEF within the heart.
Impact: Our study, involving 20 healthy volunteers, showcased outstanding reproducibility in the measurements, suggesting its potential for translation into imaging studies for patients with myocardial metabolic dysfunction.
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